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RNA-binding residues prediction using structural features

Overview of attention for article published in BMC Bioinformatics, August 2015
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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8 X users

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Title
RNA-binding residues prediction using structural features
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0691-0
Pubmed ID
Authors

Huizhu Ren, Ying Shen

Abstract

RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, it's important to identify RNA-binding residues in proteins. In this work, we propose a set of new structural features for RNA-binding residue prediction. A set of template patches are first extracted from RNA-binding interfaces. To construct structural features for a residue, we compare its surrounding patches with each template patch and use the accumulated distances as its structural features. These new features provide sufficient structural information of surrounding surface of a residue and they can be used to measure the structural similarity between the surface surrounding two residues. The new structural features, together with other sequence features, are used to predict RNA-binding residues using ensemble learning technique. The experimental results reveal the effectiveness of the proposed structural features. In addition, the clustering results on template patches exhibit distinct structural patterns of RNA-binding sites, although the sequences of template patches in the same cluster are not conserved. We speculate that RNAs may have structure preferences when binding with proteins.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 2%
United Kingdom 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 40%
Researcher 7 16%
Student > Master 4 9%
Student > Bachelor 3 7%
Student > Postgraduate 2 5%
Other 3 7%
Unknown 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 30%
Biochemistry, Genetics and Molecular Biology 12 28%
Computer Science 6 14%
Engineering 2 5%
Chemical Engineering 1 2%
Other 3 7%
Unknown 6 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 August 2015.
All research outputs
#13,210,525
of 22,821,814 outputs
Outputs from BMC Bioinformatics
#4,003
of 7,286 outputs
Outputs of similar age
#120,817
of 264,589 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 114 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,286 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,589 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.